twilight.combi: All permutations of a binary vector
In twilight: Estimation of local false discovery rate

Description Usage Arguments Details Value Author(s) References See Also Examples

For a given binary input vector, the function completely enumerates all possible permutations.

1	twilight.combi(xin, pin, bin)

`xin`	Binary input vector, e.g. class labels.
`pin`	Logical value. `TRUE` if samples are paired, `FALSE` if not.
`bin`	Logical value. `TRUE` if permutations should be balanced, `FALSE` if not.

Please note, that the resulting permutations are always as "balanced" as possible. The balancing is done for the smaller subsample. If its sample size is odd, say 5, twilight.combi computes all permutations with 2 or 3 samples unchanged. In the paired case, the output matrix contains only one half of all permutations. The second half is simply 1-output which leads to the same absolute test statistics in a paired test.

Returns a matrix where each row contains one permuted vector. Note that even for balanced permutations, the first row always contains the original vector. If the number of rows exceeds 10000, NULL is returned.

Stefanie Scheid

Scheid S and Spang R (2004): A stochastic downhill search algorithm for estimating the local false discovery rate, IEEE TCBB 1(3), 98–108.

Scheid S and Spang R (2005): twilight; a Bioconductor package for estimating the local false discovery rate, Bioinformatics 21(12), 2921–2922.

Scheid S and Spang R (2006): Permutation filtering: A novel concept for significance analysis of large-scale genomic data, in: Apostolico A, Guerra C, Istrail S, Pevzner P, and Waterman M (Eds.): Research in Computational Molecular Biology: 10th Annual International Conference, Proceedings of RECOMB 2006, Venice, Italy, April 2-5, 2006. Lecture Notes in Computer Science vol. 3909, Springer, Heidelberg, pp. 338-347.

twilight.permute.pair, twilight.permute.unpair